Four Core API Ingestion Patterns

API ingestion is not one size fits all. The pattern you choose depends on who initiates data transfer, how much control you have over timing, and what latency you need. Four patterns dominate production systems. Pull Based Polling: Your pipeline wakes up every 15 minutes or hourly and fetches data. You use updated_at timestamps or cursors to get only changed records since last sync. Fivetran uses this pattern for most SaaS connectors. Typical incremental syncs achieve p95 latency under 5 to 10 minutes, while full backfills might take hours due to pagination and rate limits. Push Based Webhooks: The source system calls your HTTP endpoint when data changes. You validate the signature, enqueue the payload into a message queue or log, and return 200 OK immediately. This can deliver sub second freshness but requires you to maintain a highly available endpoint with proper authentication. You still need occasional full syncs to catch missed events. Async Job Based: You submit a batch of updates through one API endpoint and receive a job identifier. You then poll a separate status endpoint every 10 seconds until the job completes. Bloomreach uses this pattern for product catalog ingestion, with typical job latencies of 30 to 300 seconds for tens of thousands of products. This decouples submission from processing, allowing the backend to throttle work. Streaming Event Collection: Each user action or system event is sent individually through an API to a collector service. Segment uses this for behavioral tracking, accepting tens of thousands of events per second. The collector writes to a durable log immediately, then fans out to warehouses and destinations asynchronously.